Certain computer vision systems require wide field of view. For example, in one automotive application, it is desirable to sense wide fields around the vehicle for pedestrians, vehicles, and other objects for collision warning, avoidance, and mitigation. Detecting landmarks visually and enhancing situational awareness and vehicle location accuracy is another automotive application. Very wide angle occupant sensing for security, child monitoring, and smart airbag systems are yet other examples of automotive applications. Surveillance and perimeter security are examples of other non-automotive applications. In all of these applications and others not separately mentioned, it is generally thought to be cost-effective and efficient from a packaging perspective to use a single high-resolution video sensor or camera with a wide-angle or omnivision lens or a spherical mirror/lens combination (catadioptrics) than to combine video streams from multiple video sensors or camperas looking in different directions.
Almost all computer vision object recognition algorithms assume that the optical system is space-invariant and that an object appears the same no matter where it is in the image. This may be an acceptable approximation with most conventional optics. However, certain object recognition applications may benefit from the use of wide-angle or omnivision lenses or a catadioptric arrangement in order to sense as much of the field around the camera as possible. Projections of such wide-angle scenes onto a two-dimensional image necessarily results in space-variant distortions of objects, especially near the edges of the image.
Known approaches to object recognition require either that multiple classifiers be trained for different spatial regions in the image or a computationally expensive unwarping of the entire image be performed for each frame prior to use of a conventional classifier trained for undistorted features. In unwarping the image is undistorted by compensating for radial and perspective distortions so that object shape and orientation are preserved. Since these operations must be performed for each image frame, the computational burden and cost are high and increase with increasing image resolution.